scholarly journals Importance of aerosols and shape of the cloud droplet size distribution for convective clouds and precipitation

2021 ◽  
Author(s):  
Christian Barthlott ◽  
Amirmahdi Zarboo ◽  
Takumi Matsunobu ◽  
Christian Keil
Keyword(s):  
Water Content ◽  
Turning Point ◽  
Shape Parameter ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Shape Parameters ◽  
Convective Clouds ◽  
Warm Rain ◽  
Cloud Droplet Size Distribution ◽  
Rain Formation

Abstract. The predictability of deep moist convection is subject to large uncertainties resulting from inaccurate initial and boundary data, the incomplete description of physical processes, or microphysical uncertainties. In this study, we investigate the response of convective clouds and precipitation over central Europe to varying cloud condensation nuclei (CCN) concentrations and different shape parameters of the cloud droplet size distribution (CDSD), both of which are not well constrained by observations. We systematically evaluate the relative impact of these uncertainties in realistic convection-resolving simulations for multiple cases with different synoptic controls using the new icosahedral nonhydrostatic ICON model. The results show a large systematic increase in total cloud water content with increasing CCN concentrations and narrower CDSDs together with a reduction in the total rain water content. This is related to a suppressed warm-rain formation due to a less efficient collision-coalescence process. It is shown that the evaporation at lower levels is responsible for diminishing these impacts on surface precipitation, which lies between +13 % to −16 % compared to a reference run with continental aerosol assumption. In general, the precipitation response was larger for weakly-forced cases. We also find that the overall timing of convection is not sensitive to the microphysical uncertainties applied, indicating that different rain intensities are responsible for changing precipitation totals at the ground. Furthermore, weaker rain intensities in the developing phase of convective clouds can allow for a higher convective instability at later times, which can lead to a turning point with larger rain intensities later on. The existence of such a turning point and its location in time can have a major impact on precipitation totals. In general, we find that an increase in the shape parameter can produce almost as large a variation in precipitation as a CCN increase from maritime to polluted conditions. Narrowing of the CDSD not only decreases the absolute values of autoconversion and accretion, but also decreases the relative role of the warm-rain formation in general, independent of the prevailing weather regime. We further find that increasing CCN concentrations reduces the effective radius of cloud droplets stronger than larger shape parameters. The cloud optical depth, however, reveals a similar large increase with larger shape parameters as changing the aerosol load from maritime to polluted. By the frequency of updrafts as a function of height, we show a negative aerosol effect on updraft strength, indicating that the larger water load above the freezing level in polluted conditions does not lead to an invigoration of deep convection. These findings demonstrate that both, CCN assumptions and the CDSD shape parameter, are important for quantitative precipitation forecasting and should be carefully chosen if double-moment schemes are used for modeling aerosol-cloud interactions.

scholarly journals A Model Intercomparison of CCN-Limited Tenuous Clouds in the High Arctic

2017 ◽  
Author(s):  
Robin G. Stevens ◽  
Katharina Loewe ◽  
Christopher Dearden ◽  
Antonios Dimitrelos ◽  
Anna Possner ◽  
...  
Keyword(s):  
Water Content ◽  
Size Distribution ◽  
Liquid Water ◽  
Atmospheric Stability ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Liquid Water Content ◽  
High Arctic ◽  
Model Intercomparison ◽  
Cloud Droplet Size Distribution

Abstract. We perform a model intercomparison of summertime high Arctic (> 80 N) clouds observed during the 2008 Arctic Summer Cloud Ocean Study (ASCOS) campaign, when observed cloud condensation nuclei (CCN) concentrations fell below 1 cm−3. Previous analyses have suggested that at these low CCN concentrations the liquid water content (LWC) and radiative properties of the clouds are determined primarily by the CCN concentrations, conditions that have previously been referred to as the tenuous cloud regime. The intercomparison includes results from three large eddy simulation models (UCLALES-SALSA, COSMO-LES, and MIMICA) and three numerical weather prediction models (COSMO-NWP, WRF, and UM-CASIM). We test the sensitivities of the model results to different treatments of cloud droplet activation, including prescribed cloud droplet number concentrations (CDNC) and diagnostic CCN activation based on either fixed aerosol concentrations or prognostic aerosol with in-cloud processing. There remains considerable diversity even in experiments with prescribed CDNCs and prescribed ice crystal number concentrations (ICNC). The sensitivity of mixed-phase Arctic cloud properties to changes in CDNC depends on the representation of the cloud droplet size distribution within each model, which impacts on autoconversion rates. Our results therefore suggest that properly estimating aerosol–cloud interactions requires an appropriate treatment of the cloud droplet size distribution within models, as well as in-situ observations of hydrometeor size distributions to constrain them. The results strongly support the hypothesis that the liquid water content of these clouds is CCN-limited. For the observed meteorological conditions, the cloud generally did not collapse when the CCN concentration was held constant at the relatively high CCN concentrations measured during the cloudy period, but the cloud thins or collapses as the CCN concentration is reduced. The CCN concentration at which collapse occurs varies substantially between models. Only one model predicts complete dissipation of the cloud due to glaciation, and this occurs only for the largest prescribed ICNC tested in this study. Global and regional models with either prescribed CDNCs or prescribed aerosol concentrations would not reproduce these dissipation events. Additionally, future increases in Arctic aerosol concentrations would be expected to decrease the frequency of occurrence of such cloud dissipation events, with implications for the radiative balance at the surface. Our results also show that cooling of the sea-ice surface following cloud dissipation increases atmospheric stability near the surface, further suppressing cloud formation. Therefore, this suggests that linkages between aerosol and clouds, as well as linkages between clouds, surface temperatures and atmospheric stability need to be considered for weather and climate predictions in this region.

scholarly journals Understanding the Importance of Microphysics and Macrophysics for Warm Rain in Marine Low Clouds. Part II: Heuristic Models of Rain Formation

2009 ◽  
Vol 66 (10) ◽  
pp. 2973-2990 ◽  
Author(s):  
Robert Wood ◽  
Terence L. Kubar ◽  
Dennis L. Hartmann
Keyword(s):  
Steady State ◽  
Droplet Size ◽  
Liquid Water ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Liquid Water Content ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Warm Rain ◽  
Rain Formation

Abstract Two simple heuristic model formulations for warm rain formation are introduced and their behavior explored. The first, which is primarily aimed at representing warm rain formation in shallow convective clouds, is a continuous collection model that uses an assumed cloud droplet size distribution consistent with observations as the source of embryonic drizzle drops that are then allowed to fall through a fixed cloud, accreting cloud droplets. The second, which is applicable to steady-state precipitation formation in stratocumulus, is a simple two-moment bulk autoconversion and accretion model in which cloud liquid water is removed by drizzle formation and replenished on a externally specified time scale that reflects the efficacy of turbulent overturning that characterizes stratocumulus. The models’ behavior is shown to be broadly consistent with observations from the A-Train constellation of satellites, allowing the authors to explore reasons for changing model sensitivity to microphysical and macrophysical cloud properties. The models are consistent with one another, and with the observations, in that they demonstrate that the sensitivity of rain rate to cloud droplet concentration Nd (which here represents microphysical influence) is greatest for weakly precipitating clouds (i.e., for low cloud liquid water path and/or high Nd). For the steady-state model, microphysical sensitivity is shown to strongly decrease with the ratio of replenishment to drizzle time scales. Thus, rain from strongly drizzling and/or weakly replenished clouds shows low sensitivity to microphysics. This is essentially because most precipitation in these clouds is forming via accretion rather than autoconversion. For the continuous-collection model, as cloud liquid water content increases, the precipitation rate becomes more strongly controlled by the availability of cloud liquid water than by the initial embryo size or by the cloud droplet size. The models help to explain why warm rain in marine stratocumulus clouds is sensitive to Nd but why precipitation from thicker cumulus clouds appears to be less so.

scholarly journals A model intercomparison of CCN-limited tenuous clouds in the high Arctic

2018 ◽  
Vol 18 (15) ◽  
pp. 11041-11071 ◽  
Author(s):  
Robin G. Stevens ◽  
Katharina Loewe ◽  
Christopher Dearden ◽  
Antonios Dimitrelos ◽  
Anna Possner ◽  
...  
Keyword(s):  
Water Content ◽  
Size Distribution ◽  
Liquid Water ◽  
Atmospheric Stability ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Liquid Water Content ◽  
High Arctic ◽  
Model Intercomparison ◽  
Cloud Droplet Size Distribution

Abstract. We perform a model intercomparison of summertime high Arctic (> 80∘ N) clouds observed during the 2008 Arctic Summer Cloud Ocean Study (ASCOS) campaign, when observed cloud condensation nuclei (CCN) concentrations fell below 1 cm−3. Previous analyses have suggested that at these low CCN concentrations the liquid water content (LWC) and radiative properties of the clouds are determined primarily by the CCN concentrations, conditions that have previously been referred to as the tenuous cloud regime. The intercomparison includes results from three large eddy simulation models (UCLALES-SALSA, COSMO-LES, and MIMICA) and three numerical weather prediction models (COSMO-NWP, WRF, and UM-CASIM). We test the sensitivities of the model results to different treatments of cloud droplet activation, including prescribed cloud droplet number concentrations (CDNCs) and diagnostic CCN activation based on either fixed aerosol concentrations or prognostic aerosol with in-cloud processing. There remains considerable diversity even in experiments with prescribed CDNCs and prescribed ice crystal number concentrations (ICNC). The sensitivity of mixed-phase Arctic cloud properties to changes in CDNC depends on the representation of the cloud droplet size distribution within each model, which impacts autoconversion rates. Our results therefore suggest that properly estimating aerosol–cloud interactions requires an appropriate treatment of the cloud droplet size distribution within models, as well as in situ observations of hydrometeor size distributions to constrain them. The results strongly support the hypothesis that the liquid water content of these clouds is CCN limited. For the observed meteorological conditions, the cloud generally did not collapse when the CCN concentration was held constant at the relatively high CCN concentrations measured during the cloudy period, but the cloud thins or collapses as the CCN concentration is reduced. The CCN concentration at which collapse occurs varies substantially between models. Only one model predicts complete dissipation of the cloud due to glaciation, and this occurs only for the largest prescribed ICNC tested in this study. Global and regional models with either prescribed CDNCs or prescribed aerosol concentrations would not reproduce these dissipation events. Additionally, future increases in Arctic aerosol concentrations would be expected to decrease the frequency of occurrence of such cloud dissipation events, with implications for the radiative balance at the surface. Our results also show that cooling of the sea-ice surface following cloud dissipation increases atmospheric stability near the surface, further suppressing cloud formation. Therefore, this suggests that linkages between aerosol and clouds, as well as linkages between clouds, surface temperatures, and atmospheric stability need to be considered for weather and climate predictions in this region.

scholarly journals Demistify: a large-eddy simulation (LES) and single-column model (SCM) intercomparison of radiation fog

2022 ◽  
Vol 22 (1) ◽  
pp. 319-333
Author(s):  
Ian Boutle ◽  
Wayne Angevine ◽  
Jian-Wen Bao ◽  
Thierry Bergot ◽  
Ritthik Bhattacharya ◽  
...  
Keyword(s):  
Large Eddy Simulation ◽  
Weather Prediction ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Radiation Fog ◽  
Eddy Simulation ◽  
Cloud Droplet Size Distribution ◽  
Large Eddy ◽  
Single Column ◽  
Les Model

Abstract. An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the Local and Non-local Fog Experiment (LANFEX) field campaign. Seven of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst three are research-grade SCMs designed for fog simulation, and the LESs are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number concentration (CDNC) conditions. The main SCM bias appears to be toward the overdevelopment of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parameterisation, as it is to the underlying aerosol or CDNC.

scholarly journals Demistify: an LES and SCM intercomparison of radiation fog

2021 ◽  
Author(s):  
Ian Boutle ◽  
Wayne Angevine ◽  
Jian-Wen Bao ◽  
Thierry Bergot ◽  
Ritthik Bhattacharya ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Radiation Fog ◽  
Eddy Simulation ◽  
Cloud Droplet Size Distribution ◽  
Single Column ◽  
Research Grade ◽  
Model Variability ◽  
Les Model

Abstract. An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the LANFEX field campaign. 7 of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst 3 are research-grade SCMs designed for fog simulation, and the LES are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality, and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number (CDNC) conditions. The main SCM bias appears to be toward over-development of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP-SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parametrization, as it is to the underlying aerosol or CDNC.

Warm Cloud Evolution, Precipitation, and Their Weak Linkage in HadGEM3: New Process-Level Diagnostics using A-Train Observations

2021 ◽  
Author(s):  
Hanii Takahashi ◽  
Alejandro Bodas-Salcedo ◽  
Graeme Stephens
Keyword(s):  
Large Scale ◽  
Cloud Droplet ◽  
Formation Processes ◽  
Evaluation Tool ◽  
Liquid Water Path ◽  
Microphysics Scheme ◽  
Warm Rain ◽  
Weak Linkage ◽  
The Impact ◽  
Rain Formation

AbstractThe latest configuration of the Hadley Centre Global Environmental Model version 3 (HadGEM3) contains significant changes in the formulation of warm rain processes and aerosols. We evaluate the impacts of these changes in the simulation of warm rain formation processes using A-Train observations. We introduce a new model evaluation tool, quartile-based Contoured Frequency by Optical Depth Diagrams (CFODDs), in order to fill in some blind spots that conventional CFODDs have. Results indicate that HadGEM3 has weak linkage between the size of particle radius and warm rain formation processes, and switching to the new warm rain microphysics scheme causes more difference in warm rain formation processes than switching to the new aerosol scheme through reducing overly produced drizzle mode in HadGEM3. Finally, we run an experiment in which we perturb the second aerosol indirect effect (AIE) to study the rainfall-aerosol interaction in HadGEM3. Since the large changes in the cloud droplet number concentration (CDNC) appear in the AIE experiment, a large impact in warm rain diagnostics is expected. However, regions with large fractional changes in CDNC show a muted change in precipitation, arguably because large-scale constraints act to reduce the impact of such a big change in CDNC. The adjustment in cloud liquid water path to the AIE perturbation produces a large negative shortwave forcing in the midlatitudes.

scholarly journals An Observational Study on Cloud Spectral Width in North China

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 109 ◽  
Author(s):  
Yuan Wang ◽  
Shengjie Niu ◽  
Chunsong Lu ◽  
Yangang Liu ◽  
Jingyi Chen ◽  
...  
Keyword(s):  
Size Range ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Spectral Width ◽  
Relative Dispersion ◽  
Cloud Base ◽  
Cloud Droplets ◽  
Factors Affecting ◽  
Cloud Droplet Size Distribution ◽  
Stratiform Clouds

Cloud droplet size distribution (CDSD) is a critical characteristic for a number of processes related to clouds, considering that cloud droplets are formed in different sizes above the cloud-base. This paper analyzes the in-situ aircraft measurements of CDSDs and aerosol concentration ( N a ) performed in stratiform clouds in Hebei, China, in 2015 to reveal the characteristics of cloud spectral width, commonly known as relative dispersion ( ε , ratio of standard deviation (σ) to mean radius (r) of the CDSD). A new algorithm is developed to calculate the contributions of droplets of different sizes to ε . It is found that small droplets with the size range of 1 to 5.5 μm and medium droplets with the size range of 5.5 to 10 μm are the major contributors to ε, and the medium droplets generally dominate the change of ε. The variation of ε with N a can be well explained by comparing the normalized changes of σ and r ( k σ / σ and k r / r ), rather than k σ and k r only ( k σ is Δσ/Δ N a and k r is Δr/Δ N a ). From the perspective of external factors affecting ε change, the effects of N a and condensation are examined. It is found that ε increases initially and decreases afterward as N a increases, and “condensational broadening” occurs up to 1 km above cloud-base, potentially providing observational evidence for recent numerical simulations in the literature.

scholarly journals Cloud heterogeneity on cloud and aerosol above cloud properties retrieved from simulated total and polarized reflectances

2018 ◽  
Vol 11 (6) ◽  
pp. 3627-3643 ◽  
Author(s):  
Céline Cornet ◽  
Laurent C.-Labonnote ◽  
Fabien Waquet ◽  
Frédéric Szczap ◽  
Lucia Deaconu ◽  
...  
Keyword(s):  
Size Distribution ◽  
Droplet Size ◽  
Optical Thickness ◽  
Incidence Angle ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Cloud Droplet Size Distribution ◽  
Fractional Cloud ◽  
Cloud Optical Thickness ◽  
Heterogeneity Effects

Abstract. Simulations of total and polarized cloud reflectance angular signatures such as the ones measured by the multi-angular and polarized radiometer POLDER3/PARASOL are used to evaluate cloud heterogeneity effects on cloud parameter retrievals. Effects on optical thickness, albedo, effective radius and variance of the cloud droplet size distribution and aerosol parameters above cloud are analyzed. Three different clouds that have the same mean optical thicknesses were generated: the first with a flat top, the second with a bumpy top and the last with a fractional cloud cover. At small scale (50 m), for oblique solar incidence, the illumination effects lead to higher total but also polarized reflectances. The polarized reflectances even reach values that cannot be predicted by the 1-D homogeneous cloud assumption. At the POLDER scale (7 km × 7 km), the angular signature is modified by a combination of the plane–parallel bias and the shadowing and illumination effects. In order to quantify effects of cloud heterogeneity on operational products, we ran the POLDER operational algorithms on the simulated reflectances to retrieve the cloud optical thickness and albedo. Results show that the cloud optical thickness is greatly affected: biases can reach up to −70, −50 or +40 % for backward, nadir and forward viewing directions, respectively. Concerning the albedo of the cloudy scenes, the errors are smaller, between −4.7 % for solar incidence angle of 20∘ and up to about +8 % for solar incidence angle of 60∘. We also tested the heterogeneity effects on new algorithms that allow retrieving cloud droplet size distribution and cloud top pressures and also aerosol above clouds. Contrary to the bi-spectral method, the retrieved cloud droplet size parameters are not significantly affected by the cloud heterogeneity, which proves to be a great advantage of using polarized measurements. However, the cloud top pressure obtained from molecular scattering in the forward direction can be biased up to about 60 hPa (around 550 m). Concerning the aerosol optical thickness (AOT) above cloud, the results are different depending on the available angular information. Above the fractional cloud, when only side scattering angles between 100 and 130∘ are available, the AOT is underestimated because of the plane–parallel bias. However, for solar zenith angle of 60∘ it is overestimated because the polarized reflectances are increased in forward directions.

scholarly journals Simultaneous retrieval of aerosol and cloud properties during the MILAGRO field campaign

2011 ◽  
Vol 11 (13) ◽  
pp. 6245-6263 ◽  
Author(s):  
K. Knobelspiesse ◽  
B. Cairns ◽  
J. Redemann ◽  
R. W. Bergstrom ◽  
A. Stohl
Keyword(s):  
Optical Properties ◽  
Size Distribution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Droplet Size ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Cloud Droplet Size Distribution ◽  
Distribution Parameters ◽  
Aerosol Properties

Abstract. Estimation of Direct Climate Forcing (DCF) due to aerosols in cloudy areas has historically been a difficult task, mainly because of a lack of appropriate measurements. Recently, passive remote sensing instruments have been developed that have the potential to retrieve both cloud and aerosol properties using polarimetric, multiple view angle, and multi spectral observations, and therefore determine DCF from aerosols above clouds. One such instrument is the Research Scanning Polarimeter (RSP), an airborne prototype of a sensor on the NASA Glory satellite, which unfortunately failed to reach orbit during its launch in March of 2011. In the spring of 2006, the RSP was deployed on an aircraft based in Veracruz, Mexico, as part of the Megacity Initiative: Local and Global Research Observations (MILAGRO) field campaign. On 13 March, the RSP over flew an aerosol layer lofted above a low altitude marine stratocumulus cloud close to shore in the Gulf of Mexico. We investigate the feasibility of retrieving aerosol properties over clouds using these data. Our approach is to first determine cloud droplet size distribution using the angular location of the cloud bow and other features in the polarized reflectance. The selected cloud was then used in a multiple scattering radiative transfer model optimization to determine the aerosol optical properties and fine tune the cloud size distribution. In this scene, we were able to retrieve aerosol optical depth, the fine mode aerosol size distribution parameters and the cloud droplet size distribution parameters to a degree of accuracy required for climate modeling. This required assumptions about the aerosol vertical distribution and the optical properties of the coarse aerosol size mode. A sensitivity study was also performed to place this study in the context of future systematic scanning polarimeter observations, which found that the aerosol complex refractive index can also be observed accurately if the aerosol optical depth is larger than roughly 0.8 at a wavelength of (0.555 μm).

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